Experiments on real datasets find that balancing methods increase predictive multiplicity in Rashomon sets of models, measured via ambiguity, discrepancy, and a new obscurity metric.
Chapman and Hall/CRC, New York (2021)
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An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification
Experiments on real datasets find that balancing methods increase predictive multiplicity in Rashomon sets of models, measured via ambiguity, discrepancy, and a new obscurity metric.